CN117272192B - Sewage treatment system of magnetic coagulation efficient sedimentation tank based on sewage detection - Google Patents
Sewage treatment system of magnetic coagulation efficient sedimentation tank based on sewage detection Download PDFInfo
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Abstract
The invention relates to the field of data processing, in particular to a magnetic coagulation efficient sedimentation tank sewage treatment system based on sewage detection, which comprises the following components: collecting temperature data at different positions of a magnetic coagulation efficient sedimentation zone; segmenting temperature data at each position to obtain a plurality of data intervals of the temperature data; obtaining a data change interval of each data point in each data interval, and obtaining a normal temperature data reference value according to two end point data points of the data change interval; obtaining the temperature change degree of each data point according to the data change of the data change interval and the normal temperature data reference value; obtaining a temperature evaluation value of each data point according to the temperature change degree; temperature evaluation values of data points in the obtained temperature data of the respective positions; obtaining the number of samples after adjustment of each data interval according to the temperature evaluation value of each data point; and obtaining abnormal data through an isolated forest algorithm according to the number of the samples adjusted in each data interval.
Description
Technical Field
The invention relates to the field of data processing, in particular to a magnetic coagulation efficient sedimentation tank sewage treatment system based on sewage detection.
Background
In the sewage treatment system of the magnetic coagulation efficient sedimentation tank, abnormal detection is often required to be carried out on temperature data, and one common abnormal detection algorithm is an isolated forest algorithm. The algorithm does not describe the difference of the sample from other samples by means of indicators like distance, density etc., but directly characterizes the so-called degree of combing. The number of sample sets (the number of the isolated trees) in the algorithm is always fixed, so that different problems can occur when isolated forest anomaly detection is carried out on data segments with different data distribution characteristics, the effectiveness of the algorithm is further affected, and the efficiency of the magnetic coagulation efficient sedimentation tank sewage treatment system is affected.
In the prior art, the isolated forest algorithm has certain requirements on the data distribution in the sample data, for example, for smoother data segments, if the establishment of a fixed number of isolated trees is unnecessary, the calculation amount of the algorithm is large; for highly variable data segments, where the anomaly data may be more, more orphan trees are needed to accurately calculate its anomaly score value. Meanwhile, as some temperature changes in the system are normal, the method for establishing the isolated tree based on the data size as the characteristic value in the traditional algorithm is not applicable to the current scene.
Aiming at the problems, the invention provides a magnetic coagulation efficient sedimentation tank sewage treatment system based on sewage detection. Firstly, calculating a precipitation area temperature evaluation value through regularity among collected temperature data of each position, which can be used for constructing an isolated number of characteristic values, and adaptively determining the number of sample sets according to the temperature evaluation values in the sample sets, thereby effectively improving the system efficiency.
Disclosure of Invention
In order to solve the problems, the invention provides a magnetic coagulation efficient sedimentation tank sewage treatment system based on sewage detection, which comprises:
the temperature data acquisition module is used for acquiring temperature data at different positions of the magnetic coagulation efficient sedimentation zone;
the temperature data analysis processing module is used for segmenting the temperature data at each position to obtain a plurality of data intervals of the temperature data at each position; obtaining a data change interval of each data point in each data interval, and obtaining a normal temperature data reference value according to two end point data of the data change interval; obtaining the temperature change degree of each data point according to the data change of the data change interval and the normal temperature data reference value; obtaining a first temperature evaluation value of each data point according to the temperature change degree; thresholding the first temperature evaluation value to obtain a second temperature evaluation value of each data point in the temperature data of each position; obtaining the number of samples after each data interval adjustment according to the second temperature evaluation value of each data point;
the abnormal data detection module is used for obtaining abnormal data through an isolated forest algorithm according to the number of the samples adjusted in each data interval.
Further, the step of segmenting the temperature data at each position to obtain a plurality of data intervals of the temperature data at each position includes the steps of:
and dividing the temperature data at each position according to the preset time length to obtain a plurality of data intervals of the temperature data at each position.
Further, the step of obtaining a data change interval of each data point in each data interval and obtaining a normal temperature data reference value according to two end point data of the data change interval includes the steps of:
marking any data point in each data interval as a current data point, taking the current data point as a center, extending the data point to two ends of the data point, cutting off the data point with the derivative of 0, forming a data change interval of the current data point by the extended data point, and then obtaining the data change interval of each data point in the data interval; and obtaining a reference value of the normal temperature data according to the two end point data of the data change interval.
Further, the step of obtaining the reference value of the normal temperature data according to the two end point data of the data change interval, and recording the reference value as the reference value for obtaining the normal temperature data comprises the following steps:
in the method, in the process of the invention,indicate->The first part of the temperature data>The>First mean value of two end point data of data change interval where each data point is located, +.>Indicate->The first part of the temperature data>The>Left end point data +.>Temperature value of>Indicate->The first part of the temperature data>The>Right side end point data +.>Temperature value of (2);
and then, obtaining the average value of the two end point data of the data interval where all the data points are located, solving the average value of the first average value of the two end point data of the data interval where all the data points are located, and taking the average value as the reference value of the normal temperature data.
Further, the step of obtaining the temperature change degree of each data point according to the data change of the data change interval and the normal temperature data reference value includes the following steps:
in the method, in the process of the invention,indicate->The first part of the temperature data>The>The degree of temperature change of the data points,indicate->The first part of the temperature data>The>The +.>Temperature value of data point>Indicate->The first part of the temperature data>The>The first average value of the two end point data of the data change interval where the data points are located is marked as +.>，/>Time point representing the left end point of the data change interval, < >>Time point representing right end point of data change section, +.>The section length of the data change section is represented.
Further, the step of obtaining a temperature evaluation value of each data point according to the temperature variation degree includes the steps of:
will be the firstThe first part of the temperature data>The>The degree of temperature change of the data points is recorded as +.>Will->The first part of the temperature data>The>The degree of temperature change of the data points is recorded as +.>Will->The first part of the temperature data>Starting time point record of each data intervalIs->Will->The first part of the temperature data>The starting time point of the data interval is marked +.>According to->And->Difference of->And->Difference of (2) and->The first part of the temperature data>Variance of individual data intervals gets +.>The first part of the temperature data>The>A first temperature estimate of a data point.
Further, according toAnd->Difference of->And->Difference of (2) and->The first part of the temperature data>Variance of individual data intervals gets +.>The first part of the temperature data>The>A first temperature estimate for a data point, comprising the steps of:
in the method, in the process of the invention,indicate->The first part of the temperature data>The>First temperature evaluation value of data point, +.>Indicate->The first part of the temperature data>Variance of individual data intervals>Representing a linear normalization function.
Further, the step of obtaining the temperature evaluation value of each data point in the temperature data of each position includes the steps of:
in the method, in the process of the invention,indicate->The first part of the temperature data>The>Second temperature evaluation value of data point, +.>Indicate->The first part of the temperature data>The>First temperature evaluation value of data point, +.>Represent the firstThe first part of the temperature data>The>Mean value of derivatives of five data points in left and right neighborhood of data point, +.>Representing a preset threshold.
Further, the step of obtaining the number of samples adjusted for each data interval according to the temperature evaluation value of each data point includes the steps of:
in the method, in the process of the invention,indicate->The first part of the temperature data>Sample number after adjustment of each data interval, +.>Indicate->The first part of the temperature data>The variance of the temperature evaluation values of all the data points in each data section, 10 is the amplification factor.
Further, the step of obtaining abnormal data through an isolated forest algorithm according to the number of the samples adjusted in each data interval includes the steps of:
according to the determined sample number of each data interval in each temperature data, performing anomaly detection of an isolated forest algorithm by using the temperature evaluation value as a characteristic value established by an isolated tree to obtain an anomaly scoreWhen abnormal score valueWhen (I)>And if the temperature data is a preset threshold value, marking the temperature data in the data interval as abnormal data.
The invention has the following beneficial effects: when abnormal monitoring is carried out on the temperature data of the magnetic coagulation efficient sedimentation tank through an isolated forest algorithm, the quantity of the sample sets is adaptively determined according to the change of the temperature data at different monitoring positions because the quantity of the data points contained in the sample sets relates to the accuracy of judging the temperature data, so that the monitoring on the temperature data is more accurate.
And the invention can consider the whole temperature, namely the temperature evaluation value, through the relation among the temperature data of a plurality of positions of the sedimentation zone, thereby avoiding the deviation caused by the false abnormal condition of adopting single-dimension temperature data and ensuring that the abnormal detection is more accurate. The number of the sample sets can be determined by analyzing the distribution condition of the data in the sample sets established by the isolated tree, and compared with the method for fixing the number of the sample sets in the traditional isolated forest algorithm, the method can effectively reduce the calculated amount of the algorithm and improve the accuracy and efficiency of the algorithm, thereby improving the efficiency of the magnetic coagulation efficient sedimentation tank sewage treatment system based on sewage detection.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a system block diagram of a magnetic coagulation efficient sedimentation tank sewage treatment system based on sewage detection according to an embodiment of the invention.
FIG. 2 is a diagram showing the ordering of temperature data when there is a change in temperature data at different locations according to one embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of the specific implementation, structure, characteristics and effects of the magnetic coagulation efficient sedimentation tank sewage treatment system based on sewage detection according to the invention with reference to the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The specific scheme of the sewage treatment system of the magnetic coagulation efficient sedimentation tank based on sewage detection provided by the invention is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a magnetic coagulation efficient sedimentation tank sewage treatment system based on sewage detection according to an embodiment of the invention is shown, and the system comprises the following modules:
the temperature data acquisition module 101 is used for acquiring temperature data of the magnetic coagulation efficient sedimentation zone;
in a sewage treatment system of the magnetic coagulation high-efficiency sedimentation tank, abnormal detection is often needed on temperature data, so that 5 temperature sensors are uniformly arranged in the center area of the sedimentation area in the direction vertical to the horizontal plane, temperature data at different positions of the sedimentation area are respectively acquired, sampling is carried out once at 30 seconds, and the sampling time is 12 hours. And then obtaining temperature data of 5 sensors, and respectively recording the temperature data as first temperature data, second temperature data, … and fifth temperature data according to the sequence from top to bottom, wherein the temperature data comprise a plurality of data points, and each data point represents a temperature value at each moment.
Thus, temperature data of the precipitation zone were obtained.
Temperature data analysis processing module 102:
it should be noted that, because there is a certain relation between the temperature data of each position in the sedimentation tank, for example, the temperature change of a certain position may cause the temperature change of other positions, therefore, the regularity of the temperature change of each position may be used to obtain a temperature evaluation value representing the sedimentation area in the magnetic coagulation efficient sedimentation tank, and the temperature of the sedimentation area caused by the chemical reaction may be smoothed in the calculation process, so as to prevent deviation in the anomaly detection process. Meanwhile, the preference degree of the data segment can be represented according to the data distribution of the temperature evaluation value in the data segment, and the number of sample sets in the data segment is determined in a self-adaptive mode. The specific implementation method is as follows:
a. segmenting the acquired temperature data of each position;
it should be noted that, in this embodiment, when abnormality detection is performed on temperature data in a precipitation area, an isolated forest algorithm is used, if abnormality detection is performed on all collected temperature data by using the isolated forest algorithm, a plurality of sample sets need to be randomly sampled to achieve a better detection result, even if the data at some time points may not participate in the detection. Meanwhile, for a plurality of sample data sets, the calculated amount is large when the abnormal score value is calculated; if the number of sample data in the sample set is simply increased, excessive sample data may cause the depth of the constructed tree to be too large, thereby reducing the efficiency of the algorithm. To sum up, the collected temperature data needs to be segmented according to time, and then the number of sample sets is adaptively determined according to the change of the segmented data. The calculation time of the algorithm can be reduced, and the accuracy of detecting the temperature data abnormality is improved.
Specifically, taking the first temperature data as an example, the first temperature data is divided into data sections according to the time length of each hour from the first data point, that is, each data section includes 120 data points, so as to obtain a plurality of data sections of the first temperature data. Then, a plurality of data intervals of each temperature data are obtained.
b. Calculating a temperature evaluation value through temperature data of each position, and realizing self-adaption determination of the number of sample sets;
it should be noted that, the temperature of the sedimentation area is estimated according to the sample set obtained above, and the temperature estimation can be understood as the temperature of the whole sedimentation area in the magnetic coagulation efficient sedimentation tank at the current moment. Since the temperature data at a certain position changes, the overall temperature of the precipitation zone changes, and the temperature evaluation value is relatively high, the temperature data at the position is highly likely to be abnormal. The reason for the variation of the temperature data at different positions is that chemical reactions such as oxidation-reduction reaction, acid-base neutralization reaction and the like may occur in the precipitation zone, and the chemical reactions may cause endothermic or exothermic phenomena, which may cause the temperature to vary in a local range, and the variation belongs to normal temperature variation. Therefore, the normal temperature change is distinguished through the change of the data points in different sample sets, so that the temperature evaluation value which can be used for abnormal detection is obtained, and the number of the sample sets is adaptively adjusted according to the temperature evaluation value in the data segment. The specific implementation process is as follows:
in the sewage treatment process of the magnetic coagulation high-efficiency sedimentation tank, the influence of temperature on the sewage treatment process is large, so that the control of the temperature in the system is strict. Normally, the temperature in the precipitation zone is kept at a steady state, but the temperature varies locally due to chemical reactions in the precipitation zone. In particular, for exothermic chemical reactions, the temperature in a local area should rise first, during the exothermic reaction, the temperature in the local area where the heat release occurs will not rise too fast, but will diffuse to other areas, and the temperature in other locations will rise to different degrees, which will cause corresponding changes in the temperature monitoring data in different locations. When the exothermic reaction is finished, the chemical reaction for exothermic heat absorbs heat after finishing according to the law of conservation of energy, so that the temperature gradually returns to the original level. As shown in fig. 2, in the case of temperature change of the chemical reaction at the first temperature sensor position, the temperature change at different positions has a certain hysteresis in the process, so that the temperature at the second temperature transmission position is slightly delayed from the time of the temperature change at the first temperature sensing position, and the temperature monitoring data at different positions have different intensities, so that the regularity of time intervals of the hysteresis of the temperature change at different positions is utilized, and the regularity of the temperature intensity change at different positions is used for representing the temperature evaluation value at a certain moment, and the temperature evaluation value is calculated as follows:
firstly, respectively performing curve fitting on temperature data acquired from each position by a polynomial fitting technology, and respectively marking as、/>、/>、/>、/>Wherein->Indicate->At each positionR=1, 2,3,4,5, x represents the independent variable of the curve fitting. The temperature value is recorded. Then, a fitting curve of each data interval is obtained according to the divided data intervals, and is marked as follows: />，/>Indicate->The first part of the temperature data>Fitting curve of the data interval, when +.>Within the data interval->When the data points are not changed, the derivative value of the fitting curve tends to 0, namely the absolute value of the derivative value is smaller than or equal to 0.1, and the value is an empirical value, so that an operator can set the value according to different implementation environments. And comparing the change of the neighborhood data point of the data point, wherein the neighborhood data point is five data points on the left side and the right side of the data point, when the derivative value of the neighborhood data point is also approaching to 0, the data point is considered to be a normal data point, and the temperature evaluation value is set to 0. Specially described, when->When the data points are located at the starting point or the end point of the data interval, only five data points need to be extended rightward or leftward.
Further, when the firstWithin the data interval->Data point occurrence dataWhen the derivative value of the fitting curve does not tend to 0, the data at the point is considered to change, and the degree of change of the neighborhood data point of the data point needs to be judged so as to determine the regularity of temperature change in each position, thereby obtaining a temperature evaluation value.
Specifically, by the firstWithin the data interval->Extending the data point as the center to the two ends of the data point, and forming a data change interval of the current data point by the extended data point when the data point extending to the derivative of 0 is cut off, wherein the range of the interval recorded in time is +.>. Then the average value of the two end point data points of the data change interval is obtainedWherein->Indicate->The first part of the temperature data>The>Left end point data +.>Temperature value of>Indicate->The first part of the temperature data>The>Right side end point data +.>Temperature value of (2); and then, calculating the average value of all the data change interval endpoint data points, and recording the average value of the average values as a reference value of normal temperature data.
Further, the temperature change degree of the data change section in which the data point is located is obtained. Because the change of the data in the data change interval reflects the change of the temperature in the sedimentation zone, a peak area is formed in the data curve, the obtained data change interval corresponds to the peak area, and then the peak amplitude change degree corresponding to the data point is obtained according to the change of the data point of the peak area, and the calculation formula is as follows:
in the method, in the process of the invention,indicate->The first part of the temperature data>The>The degree of temperature change of the data points,indicate->The first part of the temperature data>The>The +.>Temperature value of data point, data change interval is marked as +.>，/>Time point representing the left end point of the data change interval, < >>Time point representing right end point of data change section, +.>A section length representing a data change section; />Representing the sum of the temperature value in the data change interval where the data point is located and the difference value of the normal data point, +.>Indicating the degree of temperature change in the data section. It can be seen that the larger the temperature difference and the smaller the interval, the higher or lower the corresponding peak or trough, i.e. the larger the amplitude thereof, the greater the degree of temperature change.
Further, in the same way, find the firstA data change interval of +1 data points, wherein， />Representation->The first part of the temperature data>The number of data points in each data interval is calculated, and the temperature change degree of the data change interval is recorded as +.>The degree of temperature change is->Because the temperature data at different locations exhibit hysteresis changes, taking into account the degree of temperature change with respect to the regularity of the locations, the more regular it exhibits is indicative of the more likely it is that it is a normal temperature change caused by a chemical reaction, the lower the temperature evaluation value thereof.
Specifically, the regularity of the temperature change shows the regularity in time, that is, the data interval with larger temperature change degree should appear first, and the ratio of the temperature change degree difference of the data change interval corresponding to the adjacent time to the corresponding time position difference also has some regularity. Therefore, the temperature data of each position are ordered according to the sequence of the data change in the temperature data of each position, the largest point in the data change interval is changed earlier, as shown in fig. 2, the first temperature data is changed earlier, and therefore the ordered temperature change degreeIn the first position. The degree of temperature change at different locations in chronological order is increasingly lower. The temperature change degree of the temperature data of each position after the sorting is recorded as +.>The starting points of the corresponding data change intervals are +.>Then a temperature evaluation value of the current data point in each temperature data is obtained, and the calculation formula is as follows:
in the method, in the process of the invention,indicate->The first part of the temperature data>The>First temperature evaluation value of data point, +.>Indicate->The first part of the temperature data>Variance of individual data intervals>Indicate->The first part of the temperature data>The>Degree of temperature change of data points, +.>Indicate->The first part of the temperature data>The>Degree of temperature change of data points, +.>Indicate->The first part of the temperature data>Start time point of data interval, +.>Indicate->The first part of the temperature data>Starting time points of the data intervals; />Indicating that time is elapsed->First->The first part of the temperature data>Data interval and->In the temperature dataFirst->The degree of temperature change in the data interval changesThe ratio thereof indicates the rate of change of the degree of temperature change per unit time length, and 4 indicates the ratio other than +.>The number of remaining temperature data, +.>Representing a linear normalization function.
Further, the first temperature evaluation value of each data point in the obtained temperature data of each position has the following mathematical expression:
in the method, in the process of the invention,indicate->The first part of the temperature data>The>Second temperature evaluation value of data point, +.>Indicate->The first part of the temperature data>The>Mean value of derivatives of five data points in left and right neighborhood of data point, +.>Representing a preset threshold value, get->The value is an empirical value, and the practitioner can set the value according to different implementation environments.
After the second temperature evaluation value of each data point in each piece of temperature data is obtained, the number of sample sets is then adjusted according to the distribution of the temperature evaluation values of each data point. If the temperature evaluation value in the segment data is smoother, the accurate abnormal score value can be calculated by using smaller sample number, otherwise, if the temperature evaluation value in the data segment is more violently changed, the temperature evaluation value in the data segment needs more sample number. The intensity of the data change is thus represented by calculating the variance of the temperature evaluation values of all the data points of the data section, and then the adjustment of the number of samples is performed according to the data variance. The calculation formula is as follows:wherein->Indicate->The first part of the temperature data>Sample number after adjustment of each data interval, +.>Indicate->The first part of the temperature data>The variance of the temperature evaluation values of all the data points in each data section, 10 is the amplification factor.
The abnormal data detection module 103 obtains abnormal data through an isolated forest algorithm;
according to the determined sample number of each data interval in each temperature data, performing anomaly detection of an isolated forest algorithm by using the temperature evaluation value as a characteristic value established by an isolated tree to obtain an anomaly scoreWhen abnormal score valueWhen the sedimentation region is abnormal, marking sample data in each data interval (namely temperature data in each data interval) as abnormal data, wherein the abnormal condition of the sedimentation region is represented by ∈ ->The value is an empirical value, and the practitioner can set the value according to different implementation environments. For the abnormal temperature condition, the system can mark the abnormal temperature condition as an abnormal state and trigger a corresponding early warning mechanism, so that relevant technicians can check and process the running state of sewage precipitation in time, and the accuracy and efficiency of the sewage treatment system of the magnetic coagulation efficient sedimentation tank based on sewage detection are improved.
Thus, the magnetic coagulation high-efficiency sedimentation tank sewage treatment system based on sewage detection is completed.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (4)
1. Sewage treatment system of magnetic coagulation efficient sedimentation tank based on sewage detection, characterized in that, the system includes:
the temperature data acquisition module is used for acquiring temperature data at different positions of the magnetic coagulation efficient sedimentation zone;
the temperature data analysis processing module is used for segmenting the temperature data at each position to obtain a plurality of data intervals of the temperature data at each position; obtaining a data change interval of each data point in each data interval, and obtaining a normal temperature data reference value according to two end point data of the data change interval; obtaining the temperature change degree of each data point according to the data change of the data change interval and the normal temperature data reference value; obtaining a first temperature evaluation value of each data point according to the temperature change degree; thresholding the first temperature evaluation value to obtain a second temperature evaluation value of each data point in the temperature data of each position; obtaining the number of samples after each data interval adjustment according to the second temperature evaluation value of each data point;
the abnormal data detection module is used for obtaining abnormal data through an isolated forest algorithm according to the number of the samples adjusted in each data interval;
the method for obtaining the first temperature evaluation value of each data point according to the temperature change degree comprises the following steps:
will be the firstThe first part of the temperature data>The>The degree of temperature change of the data points is recorded as +.>Will->The first part of the temperature data>The>The degree of temperature change of the data points is recorded as +.>Will->The first part of the temperature data>The start time point of each data interval is marked as +.>Will->The first part of the temperature data>The starting time point of the data interval is marked +.>According to->And->Difference of->And->Difference of (2) and->The first part of the temperature data>Variance of individual data intervals gets +.>The first part of the temperature data>The>A first temperature estimate for a data point;
said basis isAnd->Difference of->And->Difference of (2) and->The first part of the temperature data>Variance of individual data intervals gets +.>The first part of the temperature data>The>A first temperature estimate for a data point, comprising the steps of:
in the method, in the process of the invention,indicate->The first part of the temperature data>The>A first temperature estimate for a data point,indicate->The first part of the temperature data>Variance of individual data intervals>Representing a linear normalization function;
the thresholding of the first temperature evaluation value to obtain a second temperature evaluation value for each data point in the temperature data of each location includes the steps of:
in the method, in the process of the invention,indicate->The first part of the temperature data>The>Second temperature evaluation value of data point, +.>Indicate->The first part of the temperature data>The>The average value of derivatives of five data points in the left neighborhood and the right neighborhood of the data point is obtained by respectively performing curve fitting on temperature data acquired at each position through a polynomial fitting technology, so as to obtain a fitted curve, wherein the derivatives are derivatives of the data points on the fitted curve>Indicate->The first part of the temperature data>The>First temperature evaluation value of data point, +.>Representing a preset thresholdA value;
the step of obtaining the number of samples adjusted for each data interval according to the second temperature evaluation value of each data point comprises the following steps:
in the method, in the process of the invention,indicate->The first part of the temperature data>Sample number after adjustment of each data interval, +.>Indicate->The first part of the temperature data>The variance of the second temperature evaluation values of all the data points in the data interval, 10 is the amplification factor;
the abnormal data is obtained by an isolated forest algorithm according to the number of the samples adjusted in each data interval, and the method comprises the following steps:
according to the determined sample number of each data interval in each temperature data, performing anomaly detection of an isolated forest algorithm by using the temperature evaluation value as a characteristic value established by an isolated tree to obtain an anomaly scoreWhen abnormality score +.>When (I)>And if the temperature data is a preset threshold value, marking the temperature data in the data interval as abnormal data.
2. The magnetic coagulation high-efficiency sedimentation tank sewage treatment system based on sewage detection of claim 1, wherein the steps for segmenting the temperature data at each location to obtain a plurality of data intervals of the temperature data at each location, comprise the steps of:
and dividing the temperature data at each position according to the preset time length to obtain a plurality of data intervals of the temperature data at each position.
3. The sewage treatment system of the magnetic coagulation high-efficiency sedimentation tank based on sewage detection according to claim 1, wherein the step of obtaining a data change interval of each data point in each data interval and obtaining a normal temperature data reference value according to two end point data of the data change interval comprises the following steps:
marking any data point in each data interval as a current data point, taking the current data point as a center, extending the data point to two ends of the data point, cutting off the data point with the derivative of 0, forming a data change interval of the current data point by the extended data point, and then obtaining the data change interval of each data point in the data interval; obtaining a reference value of normal temperature data according to the two end point data of the data change interval;
the method for obtaining the reference value of the normal temperature data according to the two end point data of the data change interval comprises the following steps:
in the method, in the process of the invention,indicate->The first part of the temperature data>The>First mean value of two end point data of data change interval where each data point is located, +.>Indicate->The first part of the temperature data>The>Left end point data +.>Temperature value of>Indicate->The first part of the temperature data>The>Right side end point data +.>Temperature value of (2);
and then obtaining the first average value of the two end point data of the data change interval where all the data points are located, solving the average value of the first average value of the two end point data of the data change interval where all the data points are located, and taking the average value as a reference value of normal temperature data.
4. The sewage treatment system of the magnetic coagulation high-efficiency sedimentation tank based on sewage detection according to claim 1, wherein the step of obtaining the temperature change degree of each data point according to the data change of the data change interval and the normal temperature data reference value comprises the following steps:
in the method, in the process of the invention,indicate->The first part of the temperature data>The>The degree of temperature change of the data points,indicate->The first part of the temperature data>The>The +.>Temperature value of data point>Indicate->The first part of the temperature data>The>The first average value of the two end point data of the data change interval where the data points are located is marked as +.>，/>Time point representing the left end point of the data change interval, < >>Time point representing right end point of data change section, +.>The section length of the data change section is represented.
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